Deep feature generative adversarial neural networks

ABSTRACT

A mobile device can implement a neural network-based domain transfer scheme to modify an image in a first domain appearance to a second domain appearance. The domain transfer scheme can be configured to detect an object in the image, apply an effect to the image, and blend the image using color space adjustments and blending schemes to generate a realistic result image. The domain transfer scheme can further be configured to efficiently execute on the constrained device by removing operational layers based on resources available on the mobile device.

CLAIM OF PRIORITY

This application is a continuation of U.S. patent application Ser. No.16/376,564, filed Apr. 5, 2019, which is incorporated herein byreference in its entirety.

TECHNICAL FIELD

The present disclosure generally relates to machines configured to thetechnical field of special-purpose machines that manage electronic imageprocessing and improvements to such variants, and to the technologies bywhich such special-purpose machines become improved compared to otherspecial-purpose machines for performing data transformations usingneural networks.

BACKGROUND

Machine learning schemes enable computers to perform image manipulationprocesses. However, many image manipulation techniques are complex andhave large computational requirements, which make them ill-suited forimplementation on mobile devices, such as smartphones.

BRIEF DESCRIPTION OF THE DRAWINGS

To easily identify the discussion of any particular element or act, themost significant digit or digits in a reference number refer to thefigure (“FIG.”) number in which that element or act is first introduced.

FIG. 1 is a block diagram showing an example messaging system forexchanging data (e.g., messages and associated content) over a network.

FIG. 2 is block diagram illustrating further details regarding amessaging system having an integrated virtual object machine learningsystem, according to example embodiments.

FIG. 3 is a schematic diagram illustrating data which may be stored in adatabase of a messaging server system, according to certain exampleembodiments.

FIG. 4 is a schematic diagram illustrating a structure of a message,according to some embodiments, generated by a messaging clientapplication for communication.

FIG. 5 is a schematic diagram illustrating an example access-limitingprocess, in terms of which access to content (e.g., an ephemeralmessage, and associated multimedia payload of data) or a contentcollection (e.g., an ephemeral message story) may be time-limited (e.g.,made ephemeral).

FIG. 6 shows example internal functional engines of a deep-featureadversarial system, according to some example embodiments.

FIG. 7 shows a flow diagram of an example method for generating modifieddata items using a deep-feature adversarial system, according to someexample embodiments.

FIG. 8 shows a data architecture for training a deep-feature adversarialneural network according to some example embodiments.

FIG. 9 shows an example deep-feature adversarial neural networkarchitecture for first stage training, according to some exampleembodiments.

FIG. 10 shows an example deep-feature adversarial neural networkarchitecture for second stage training, according to some exampleembodiments.

FIGS. 11-13 show an example user interface for using a deep-featureadversarial system, according to some example embodiments.

FIG. 14 is a block diagram illustrating a representative softwarearchitecture, which may be used in conjunction with various hardwarearchitectures herein described.

FIG. 15 is a block diagram illustrating components of a machine,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative embodiments of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of variousembodiments of the inventive subject matter. It will be evident,however, to those skilled in the art, that embodiments of the inventivesubject matter may be practiced without these specific details. Ingeneral, well-known instruction instances, protocols, structures, andtechniques are not necessarily shown in detail.

As discussed, some image manipulation techniques are computationallycomplex and ill-suited for implementation on low-resource computers,such as a mobile device with limited memory and processing power. Anexample of a complex image manipulation technique includes neuralnetwork-based image domain transfer in which a convolutional neuralnetwork is trained to transfer an image from its native domain (e.g.,appearance) a target domain. For instance, an image domain transferneural network can be trained to transfer a camera image of a day timescene into a nighttime scene, or transfer an image of a young person toan older version of the same person. Image domain transfer neuralnetworks can be trained on large sets of example image data (e.g.,thousands of images of a given target domain, thousands of images ofnight time scene, thousands of images older people, thousands of imagesof smiling people, etc.), and during training the neural network usesthe multiple images in the large set to train and capture detailsexhibited in the example training images. While image domain transferneural networks can yield impressive results, implementing such schemeson a mobile device (e.g., a smartphone) is not practical because theprocessing time would be very long and result in poor user experience.Further, applying such schemes in live video (e.g., applying an effectto each frame of a video) on the mobile device in real time is even moreimpractical. Some conventional approaches rely on sending the images toa server for complex image processing, and then displaying the result onthe client device. However, relying on server support requires networkconnections which may not be available.

To this end, a deep-feature adversarial system can implement compleximage manipulation schemes, such as neural network-based domaintransfer, on user devices using an adversarially trained transformationsubnetwork that operates between encoder and decoder layers. In thebelow example embodiments, the domain transfer technique discussed isthat of user facial feature-based domain transfer in which an image of auser's face is modified to apply a different expression (e.g., from afrown expression to a smile expression) or appearance (e.g., from ayouthful appearance to an elderly appearance). One of ordinary skill inthe art appreciates that additional complex data manipulation schemesother than image domain transfer may likewise be implemented. Forexample, the deep-feature adversarial system can be trained to transfera sound recording from an initial domain to a domain of a certain musicgenre or style (e.g., Salsa music), as discussed in further detailbelow. Further, it is appreciated that each network can be a subnetworkor layer of a larger network that comprises a plurality of layers. Forexample, a domain transfer neural network can comprise a generator and adiscriminator, each of which can be referred to as a layer (e.g.,generator layer, discriminator layer), subnetwork (e.g., generatorsubnet, discriminator subnet), and so forth, and the entire network maybe trained using end-to-end schemes, such as back propagation.

Unlike some conventional approaches, the deep-feature adversarial systemis trained in two stages: a first generator training stage and a secondtransformation training stage. In the first stage, a generatorcomprising an encoder network followed by a decoder network is trained.In some example embodiments, the generator network is trained separately(e.g., encoder network first, decoder network last), or jointly (e.g.,encoder and decoder networks trained at the same time using the sameloss function).

At a high level, the encoder is trained to transform raw object features(e.g. pixels of images) to middle-level and high-level featurerepresentations that describe the whole input object or large parts ofit in deep-feature space, whereas the decoder network is trained toreconstruct the original input object from the middle-level orhigh-level feature representations generated by the encoder.

In the second stage, a transformation subnetwork is trained to apply atransformation effect to input data (e.g., data generated by the encoderlayer), according to some example embodiments. The subnet isadversarially trained using a discrimination network that is separatedfrom the transformation subnetwork by the decoder network, according tosome example embodiments. The encoder-decoder networks are fixed in thesecond stage in that they are already trained, and only thetransformation subnetwork is trained in the second stage.

Adversarial training involves a generator network that generates asample and a discriminator network that estimates the probability thatthe sample came from training data set of the target domain rather thanthe generator network. In other words, the discriminator network triesto differentiate between real data of the target subdomain (e.g., anight time scene, older person appearance, Salsa style music) andtransformed images generated by the generator network. In some exampleembodiments, the generator network and the discriminator network aretrained at the same time via end-to-end back propagation in which thegenerator network tries to make the error of the discriminator networkas high as possible, thereby improving the generator network output datain mimicking real objects from the target domain.

In some example embodiments, adversarial training is only performed inthe second stage to train the transformation subnetwork using thepretrained encoder and decoder networks. This prevents the transformersubnetwork from generating adversarial examples for the discriminatornetwork, which are confidently classified by the discriminator networkas real with high confidence, but in fact contain significant artifactsthat would be noticeable by a human observer. Accordingly, by performingadversarial training after the generator network is fixed, thedeep-feature adversarial system can generate high quality results thataccurately simulate domain appearance features of the target domainwithout noticeable artifacts.

In some example embodiments, the pretraining of the generator in thefirst stage further allows the deep-feature adversarial system to notuse optimized loss function data that would keep transformed objectssimilar to the original input objects, e.g. such as cycle loss. Further,in some example embodiments, second stage training does not proceedfurther than necessary to obtain a high misclassification score on thediscriminator. In this way, the produced results have a stronger effectthan those of past approaches, while still preserving the collateralfeatures that are not relevant to the differences between the sourcedomain (e.g., an image in its native style, a day time scene) and atarget domain (e.g., a night time scene). Collateral features aregenerally those that may be significant to complete the image orrecording, but are generally not modified to transfer between domains.For example, if transferring an image of a youthful person to theappearance of an older person, the eye color may be a collateral featuresince it may not change over time, whereas skin texture, wrinklequantity, and other features may be modified to generate a high-qualityimage result with strong older person visual attributes.

FIG. 1 shows a block diagram of an example messaging system 100 forexchanging data (e.g., social media posts created using the modifiedimage sequence) over a network 106. The messaging system 100 includesmultiple client devices 102, each of which hosts a number ofapplications including a messaging client application 104. Eachmessaging client application 104 is communicatively coupled to otherinstances of the messaging client application 104 and a messaging serversystem 108 via a network 106 (e.g., the Internet).

Accordingly, each messaging client application 104 is able tocommunicate and exchange data with another messaging client application104 and with the messaging server system 108 via the network 106. Thedata exchanged between messaging client applications 104, and between amessaging client application 104 and the messaging server system 108,includes functions (e.g., commands to invoke functions) as well aspayload data (e.g., text, audio, video, or other multimedia data).

The messaging server system 108 provides server-side functionality viathe network 106 to a particular messaging client application 104. Whilecertain functions of the messaging system 100 are described herein asbeing performed by either a messaging client application 104 or by themessaging server system 108, it will be appreciated that the location ofcertain functionality within either the messaging client application 104or the messaging server system 108 is a design choice. For example, itmay be technically preferable to initially deploy certain technology andfunctionality within the messaging server system 108, and to latermigrate this technology and functionality to the messaging clientapplication 104 where a client device 102 has a sufficient processingcapacity.

The messaging server system 108 supports various services and operationsthat are provided to the messaging client application 104. Suchoperations include transmitting data to, receiving data from, andprocessing data generated by the messaging client application 104. Thisdata may include message content, client device information, geolocationinformation, media annotation and overlays, message content persistenceconditions, social network information, and live event information, asexamples. Data exchanges within the messaging system 100 are invoked andcontrolled through functions available via user interfaces (UIs) of themessaging client application 104.

Turning now specifically to the messaging server system 108, anapplication programming interface (API) server 110 is coupled to, andprovides a programmatic interface to, an application server 112. Theapplication server 112 is communicatively coupled to a database server118, which facilitates access to a database 120 in which is stored dataassociated with messages processed by the application server 112.

The API server 110 receives and transmits message data (e.g., commandsand message payloads) between the client devices 102 and the applicationserver 112. Specifically, the API server 110 provides a set ofinterfaces (e.g., routines and protocols) that can be called or queriedby the messaging client application 104 in order to invoke functionalityof the application server 112. The API server 110 exposes variousfunctions supported by the application server 112, including accountregistration; login functionality; the sending of messages, via theapplication server 112, from a particular messaging client application104 to another messaging client application 104; the sending of mediafiles (e.g., images or video) from a messaging client application 104 toa messaging server application 114 for possible access by anothermessaging client application 104; the setting of a collection of mediadata (e.g., a story); the retrieval of such collections; the retrievalof a list of friends of a user of a client device 102; the retrieval ofmessages and content; the adding and deletion of friends to and from asocial graph; the location of friends within the social graph; andopening application events (e.g., relating to the messaging clientapplication 104).

The application server 112 hosts a number of applications andsubsystems, including the messaging server application 114, an imageprocessing system 116, a social network system 122, and a server-sidedeep-feature adversarial system 150. The messaging server application114 implements a number of message-processing technologies andfunctions, particularly related to the aggregation and other processingof content (e.g., textual and multimedia content) included in messagesreceived from multiple instances of the messaging client application104. As will be described in further detail, the text and media contentfrom multiple sources may be aggregated into collections of content(e.g., called stories or galleries). These collections are then madeavailable, by the messaging server application 114, to the messagingclient application 104. Other processor- and memory-intensive processingof data may also be performed server-side by the messaging serverapplication 114, in view of the hardware requirements for suchprocessing.

The application server 112 also includes the image processing system116, which is dedicated to performing various image processingoperations, typically with respect to images or video received withinthe payload of a message at the messaging server application 114.

The social network system 122 supports various social networkingfunctions and services, and makes these functions and services availableto the messaging server application 114. To this end, the social networksystem 122 maintains and accesses an entity graph (e.g., entity graph304 in FIG. 3 ) within the database 120. Examples of functions andservices supported by the social network system 122 include theidentification of other users of the messaging system 100 with whom aparticular user has relationships or whom the particular user is“following,” and also the identification of other entities and interestsof a particular user.

The server-side deep-feature adversarial system 150 is a server-sideinstance that is configured to perform training of neural network modelsin first and second stages, as discussed below, according to someexample embodiments. The server-side deep-feature adversarial system 150may differ from the deep-feature adversarial system 210 executing on theclient device 102 in that the server-side deep-feature adversarialsystem 150 manages training the models and the deep-feature adversarialsystem 210 manages implementing the models to generate modified dataitems, according to some example embodiments. In some exampleembodiments, the deep feature adversarial system can be implemented onnon-mobile devices, such as a laptop or desktop computer, a server, andso forth, and it is appreciated that the below examples are not limitedto mobile device implementations.

The application server 112 is communicatively coupled to a databaseserver 118, which facilitates access to a database 120 in which isstored data associated with messages processed by the messaging serverapplication 114.

FIG. 2 is block diagram illustrating further details regarding themessaging system 100, according to example embodiments. Specifically,the messaging system 100 is shown to comprise the messaging clientapplication 104 and the application server 112, which in turn embody anumber of subsystems, namely an ephemeral timer system 202, a collectionmanagement system 204, an annotation system 206, and a deep-featureadversarial system 210.

The ephemeral timer system 202 is responsible for enforcing thetemporary access to content permitted by the messaging clientapplication 104 and the messaging server application 114. To this end,the ephemeral timer system 202 incorporates a number of timers that,based on duration and display parameters associated with a message orcollection of messages (e.g., a social media real-time sequence of userposts, a “story”), selectively display and enable access to messages andassociated content via the messaging client application 104. Furtherdetails regarding the operation of the ephemeral timer system 202 areprovided below.

The collection management system 204 is responsible for managingcollections of media (e.g., collections of text, image, video, and audiodata). In some examples, a collection of content (e.g., messages,including images, video, text, and audio) may be organized into an“event gallery” or an “event story.” Such a collection may be madeavailable for a specified time period, such as the duration of an eventto which the content relates. For example, content relating to a musicconcert may be made available as a “story” for the duration of thatmusic concert. The collection management system 204 may also beresponsible for publishing an icon that provides notification of theexistence of a particular collection to the user interface of themessaging client application 104.

The collection management system 204 furthermore includes a curationinterface 208 that allows a collection manager to manage and curate aparticular collection of content. For example, the curation interface208 enables an event organizer to curate a collection of contentrelating to a specific event (e.g., delete inappropriate content orredundant messages). Additionally, the collection management system 204employs machine vision (or image recognition technology) and contentrules to automatically curate a content collection. In certainembodiments, compensation may be paid to a user for inclusion ofuser-generated content into a collection. In such cases, the curationinterface 208 operates to automatically make payments to such users forthe use of their content.

The annotation system 206 provides various functions that enable a userto annotate or otherwise modify or edit media content associated with amessage. For example, the annotation system 206 provides functionsrelated to the generation and publishing of media overlays for messagesprocessed by the messaging system 100. The annotation system 206operatively supplies a media overlay (e.g., a real-time video filterapplied to each video frame) to the messaging client application 104based on a geolocation of the client device 102. In another example, theannotation system 206 operatively supplies a media overlay to themessaging client application 104 based on other information, such associal network information of the user of the client device 102. A mediaoverlay may include audio and visual content and visual effects.Examples of audio and visual content include pictures, text, logos,animations, and sound effects. An example of a visual effect includescolor overlaying. The audio and visual content or the visual effects canbe applied to a media content item (e.g., a photo) at the client device102. For example, the media overlay includes text that can be overlaidon top of a photograph generated by the client device 102. In anotherexample, the media overlay includes an identification of a location(e.g., Venice Beach), a name of a live event, or a name of a merchant(e.g., Beach Coffee House). In another example, the annotation system206 uses the geolocation of the client device 102 to identify a mediaoverlay that includes the name of a merchant at the geolocation of theclient device 102. The media overlay may include other indiciaassociated with the merchant. The media overlays may be stored in thedatabase 120 and accessed through the database server 118.

In one example embodiment, the annotation system 206 provides auser-based publication platform that enables users to select ageolocation on a map and upload content associated with the selectedgeolocation. The user may also specify circumstances under whichparticular content should be offered to other users. The annotationsystem 206 generates a media overlay that includes the uploaded contentand associates the uploaded content with the selected geolocation.

In another example embodiment, the annotation system 206 provides amerchant-based publication platform that enables merchants to select aparticular media overlay associated with a geolocation via a biddingprocess. For example, the annotation system 206 associates the mediaoverlay of a highest-bidding merchant with a corresponding geolocationfor a predefined amount of time.

The deep-feature adversarial system 210 is a client-side instance thatis configured to transform data objects from an initial domain to atarget domain using an adversarially trained deep feature neuralnetwork, as discussed in further detail below.

FIG. 3 is a schematic diagram illustrating data 300 which may be storedin the database 120 of the messaging server system 108, according tocertain example embodiments. While the content of the database 120 isshown to comprise a number of tables, it will be appreciated that thedata could be stored in other types of data structures (e.g., as anobject-oriented database).

The database 120 includes message data stored within a message table314. An entity table 302 stores entity data, including an entity graph304. Entities for which records are maintained within the entity table302 may include individuals, corporate entities, organizations, objects,places, events, and so forth. Regardless of type, any entity regardingwhich the messaging server system 108 stores data may be a recognizedentity. Each entity is provided with a unique identifier, as well as anentity type identifier (not shown).

The entity graph 304 furthermore stores information regardingrelationships and associations between or among entities. Suchrelationships may be social, professional (e.g., work at a commoncorporation or organization), interest-based, or activity-based, forexample.

The database 120 also stores annotation data, in the example form offilters, in an annotation table 312. Filters for which data is storedwithin the annotation table 312 are associated with and applied tovideos (for which data is stored in a video table 310) and/or images(for which data is stored in an image table 308). Filters, in oneexample, are overlays that are displayed as overlaid on an image orvideo during presentation to a recipient user. Filters may be of varioustypes, including user-selected filters from a gallery of filterspresented to a sending user by the messaging client application 104 whenthe sending user is composing a message. Other types of filters includegeolocation filters (also known as geo-filters), which may be presentedto a sending user based on geographic location. For example, geolocationfilters specific to a neighborhood or special location may be presentedwithin a user interface by the messaging client application 104, basedon geolocation information determined by a Global Positioning System(GPS) unit of the client device 102. Another type of filter is a datafilter, which may be selectively presented to a sending user by themessaging client application 104, based on other inputs or informationgathered by the client device 102 during the message creation process.Examples of data filters include a current temperature at a specificlocation, a current speed at which a sending user is traveling, abattery life for a client device 102, or the current time.

Other annotation data that may be stored within the image table 308 isso-called “lens” data. A “lens” may be a real-time special effect andsound that may be added to an image or a video.

As mentioned above, the video table 310 stores video data which, in oneembodiment, is associated with messages for which records are maintainedwithin the message table 314. Similarly, the image table 308 storesimage data associated with messages for which message data is stored inthe message table 314. The entity table 302 may associate variousannotations from the annotation table 312 with various images and videosstored in the image table 308 and the video table 310.

A story table 306 stores data regarding collections of messages andassociated image, video, or audio data, which are compiled into acollection (e.g., a story or a gallery). The creation of a particularcollection may be initiated by a particular user (e.g., each user forwhom a record is maintained in the entity table 302). A user may createa “personal story” in the form of a collection of content that has beencreated and sent/broadcast by that user. To this end, the user interfaceof the messaging client application 104 may include an icon that isuser-selectable to enable a sending user to add specific content to hisor her personal story.

A collection may also constitute a “live story,” which is a collectionof content from multiple users that is created manually, automatically,or using a combination of manual and automatic techniques. For example,a “live story” may constitute a curated stream of user-submitted contentfrom various locations and events. Users whose client devices 102 havelocation services enabled and are at a common location or event at aparticular time may, for example, be presented with an option, via auser interface of the messaging client application 104, to contributecontent to a particular live story. The live story may be identified tothe user by the messaging client application 104 based on his or herlocation. The end result is a “live story” told from a communityperspective.

A further type of content collection is known as a “location story,”which enables a user whose client device 102 is located within aspecific geographic location (e.g., on a college or university campus)to contribute to a particular collection. In some embodiments, acontribution to a location story may require a second degree ofauthentication to verify that the end user belongs to a specificorganization or other entity (e.g., is a student on the universitycampus).

FIG. 4 is a schematic diagram illustrating a structure of a message 400,according to some embodiments, generated by a messaging clientapplication 104 for communication to a further messaging clientapplication 104 or the messaging server application 114. The content ofa particular message 400 is used to populate the message table 314stored within the database 120, accessible by the messaging serverapplication 114. Similarly, the content of a message 400 is stored inmemory as “in-transit” or “in-flight” data of the client device 102 orthe application server 112. The message 400 is shown to include thefollowing components:

-   -   A message identifier 402: a unique identifier that identifies        the message 400.    -   A message text payload 404: text, to be generated by a user via        a user interface of the client device 102 and that is included        in the message 400.    -   A message image payload 406: image data captured by a camera        component of a client device 102 or retrieved from memory of a        client device 102, and that is included in the message 400.    -   A message video payload 408: video data captured by a camera        component or retrieved from a memory component of the client        device 102, and that is included in the message 400.    -   A message audio payload 410: audio data captured by a microphone        or retrieved from the memory component of the client device 102,        and that is included in the message 400.    -   Message annotations 412: annotation data (e.g., filters,        stickers, or other enhancements) that represents annotations to        be applied to the message image payload 406, message video        payload 408, or message audio payload 410 of the message 400.    -   A message duration parameter 414: a parameter value indicating,        in seconds, the amount of time for which content of the message        400 (e.g., the message image payload 406, message video payload        408, and message audio payload 410) is to be presented or made        accessible to a user via the messaging client application 104.    -   A message geolocation parameter 416: geolocation data (e.g.,        latitudinal and longitudinal coordinates) associated with the        content payload of the message 400. Multiple message geolocation        parameter 416 values may be included in the payload, with each        of these parameter values being associated with respective        content items included in the content (e.g., a specific image in        the message image payload 406, or a specific video in the        message video payload 408).    -   A message story identifier 418: identifies values identifying        one or more content collections (e.g., “stories”) with which a        particular content item in the message image payload 406 of the        message 400 is associated. For example, multiple images within        the message image payload 406 may each be associated with        multiple content collections using identifier values.    -   A message tag 420: one or more tags, each of which is indicative        of the subject matter of content included in the message        payload. For example, where a particular image included in the        message image payload 406 depicts an animal (e.g., a lion), a        tag value may be included within the message tag 420 that is        indicative of the relevant animal. Tag values may be generated        manually, based on user input, or may be automatically generated        using, for example, image recognition.    -   A message sender identifier 422: an identifier (e.g., a        messaging system identifier, email address, or device        identifier) indicative of a user of the client device 102 on        which the message 400 was generated and from which the message        400 was sent.    -   A message receiver identifier 424: an identifier (e.g., a        messaging system identifier, email address, or device        identifier) indicative of a user of the client device 102 to        which the message 400 is addressed.

The contents (e.g., values) of the various components of the message 400may be pointers to locations in tables within which content data valuesare stored. For example, an image value in the message image payload 406may be a pointer to (or address of) a location within the image table308. Similarly, values within the message video payload 408 may point todata stored within the video table 310, values stored within the messageannotations 412 may point to data stored in the annotation table 312,values stored within the message story identifier 418 may point to datastored in the story table 306, and values stored within the messagesender identifier 422 and the message receiver identifier 424 may pointto user records stored within the entity table 302.

FIG. 5 is a schematic diagram illustrating an access-limiting process500, in terms of which access to content (e.g., an ephemeral message502, and associated multimedia payload of data) or a content collection(e.g., an ephemeral message story 504) may be time-limited (e.g., madeephemeral).

An ephemeral message 502 is shown to be associated with a messageduration parameter 506, the value of which determines an amount of timethat the ephemeral message 502 will be displayed to a receiving user ofthe ephemeral message 502 by the messaging client application 104. Inone embodiment, where the messaging client application 104 is anapplication client, an ephemeral message 502 is viewable by a receivinguser for up to a maximum of 10 seconds, depending on the amount of timethat the sending user specifies using the message duration parameter506.

The message duration parameter 506 and the message receiver identifier424 are shown to be inputs to a message timer 512, which is responsiblefor determining the amount of time that the ephemeral message 502 isshown to a particular receiving user identified by the message receiveridentifier 424. In particular, the ephemeral message 502 will only beshown to the relevant receiving user for a time period determined by thevalue of the message duration parameter 506. The message timer 512 isshown to provide output to a more generalized ephemeral timer system202, which is responsible for the overall timing of display of content(e.g., an ephemeral message 502) to a receiving user.

The ephemeral message 502 is shown in FIG. 5 to be included within anephemeral message story 504 (e.g., a personal story, or an event story).The ephemeral message story 504 has an associated story durationparameter 508, a value of which determines a time duration for which theephemeral message story 504 is presented and accessible to users of themessaging system 100. The story duration parameter 508, for example, maybe the duration of a music concert, where the ephemeral message story504 is a collection of content pertaining to that concert.Alternatively, a user (either the owning user or a curator user) mayspecify the value for the story duration parameter 508 when performingthe setup and creation of the ephemeral message story 504.

Additionally, each ephemeral message 502 within the ephemeral messagestory 504 has an associated story participation parameter 510, a valueof which determines the duration of time for which the ephemeral message502 will be accessible within the context of the ephemeral message story504. Accordingly, a particular ephemeral message 502 may “expire” andbecome inaccessible within the context of the ephemeral message story504, prior to the ephemeral message story 504 itself expiring in termsof the story duration parameter 508. The story duration parameter 508,story participation parameter 510, and message receiver identifier 424each provide input to a story timer 514, which operationally determineswhether a particular ephemeral message 502 of the ephemeral messagestory 504 will be displayed to a particular receiving user and, if so,for how long. Note that the ephemeral message story 504 is also aware ofthe identity of the particular receiving user as a result of the messagereceiver identifier 424.

Accordingly, the story timer 514 operationally controls the overalllifespan of an associated ephemeral message story 504, as well as anindividual ephemeral message 502 included in the ephemeral message story504. In one embodiment, each and every ephemeral message 502 within theephemeral message story 504 remains viewable and accessible for a timeperiod specified by the story duration parameter 508. In a furtherembodiment, a certain ephemeral message 502 may expire, within thecontext of the ephemeral message story 504, based on a storyparticipation parameter 510. Note that a message duration parameter 506may still determine the duration of time for which a particularephemeral message 502 is displayed to a receiving user, even within thecontext of the ephemeral message story 504. Accordingly, the messageduration parameter 506 determines the duration of time that a particularephemeral message 502 is displayed to a receiving user, regardless ofwhether the receiving user is viewing that ephemeral message 502 insideor outside the context of an ephemeral message story 504.

The ephemeral timer system 202 may furthermore operationally remove aparticular ephemeral message 502 from the ephemeral message story 504based on a determination that it has exceeded an associated storyparticipation parameter 510. For example, when a sending user hasestablished a story participation parameter 510 of 24 hours fromposting, the ephemeral timer system 202 will remove the relevantephemeral message 502 from the ephemeral message story 504 after thespecified 24 hours. The ephemeral timer system 202 also operates toremove an ephemeral message story 504 either when the storyparticipation parameter 510 for each and every ephemeral message 502within the ephemeral message story 504 has expired, or when theephemeral message story 504 itself has expired in terms of the storyduration parameter 508.

In certain use cases, a creator of a particular ephemeral message story504 may specify an indefinite story duration parameter 508. In thiscase, the expiration of the story participation parameter 510 for thelast remaining ephemeral message 502 within the ephemeral message story504 will determine when the ephemeral message story 504 itself expires.In this case, a new ephemeral message 502, added to the ephemeralmessage story 504, with a new story participation parameter 510,effectively extends the life of an ephemeral message story 504 to equalthe value of the story participation parameter 510.

In response to the ephemeral timer system 202 determining that anephemeral message story 504 has expired (e.g., is no longer accessible),the ephemeral timer system 202 communicates with the messaging system100 (e.g., specifically, the messaging client application 104) to causean indicium (e.g., an icon) associated with the relevant ephemeralmessage story 504 to no longer be displayed within a user interface ofthe messaging client application 104. Similarly, when the ephemeraltimer system 202 determines that the message duration parameter 506 fora particular ephemeral message 502 has expired, the ephemeral timersystem 202 causes the messaging client application 104 to no longerdisplay an indicium (e.g., an icon or textual identification) associatedwith the ephemeral message 502.

FIG. 6 shows example internal functional engines of the deep-featureadversarial system 210, according to some example embodiments. Asillustrated, the deep-feature adversarial system 210 comprises aninterface engine 605, a training engine 610, a sensor engine 615, and aneural network engine 620. The interface engine 605 manages receivinginputs and displaying user interface content on a user device, such as aclient device 102. The training engine 610 is configured to train adeep-feature adversarial network to generate modified data items, suchas a modified image, a modified image sequence (e.g., video), a modifiedaudio recording, and so on. The sensor engine 615 is configured togenerate the data items for modification by the neural network engine620. For example, the sensor engine 615 is configured to use an imagesensor of a user device (e.g., mobile phone) to generate images forprocessing and modification; or use a microphone of a user device torecord an audio recording for processing and modification. The neuralnetwork engine 620 is configured to apply a deep-feature adversariallytrained neural network to a data item to generate a modified data item,as discussed in further detail below. In some example embodiments, oneor more of the engines may be located in the server-side deep-featureadversarial system 150 or the deep-feature adversarial system 210 on theclient device. For example, in some example embodiments, the trainingengine 610 is hosted solely on the server-side deep-feature adversarialsystem 150, which is then used to train models that are distributed viathe network 106 for execution by the neural network engine 620 on theclient device 102.

FIG. 7 shows a flow diagram of an example method 700 for generatingmodified data items using a deep-feature adversarial system 210,according to some example embodiments. At operation 705, the trainingengine 610 trains a deep-feature adversarial neural network to generatemodified data items, as discussed in further detail with reference toFIGS. 8, 9, and 10 . At operation 710, the sensor engine 615 generates adata item, such as an image or an audio recording. At operation 715, theneural network engine 620 generates a modified data item by applying thetrained deep-feature adversarial neural network model to the data item.At operation 720, the interface engine 605 publishes the modified dataitem to a network site as an ephemeral message, according to someexample embodiments.

FIG. 8 shows a data architecture 800 for training a deep-featureadversarial neural network according to some example embodiments. In theexample of FIG. 8 , an image is the item being modified, however it isappreciated the data architecture can be configured to modify differenttypes of data items (e.g., music data) using generative neural networks.At a high level, in the first stage 805, the generator components aretrained to encode and decode an image, and in the second stage 810, thegenerator components are fixed and a transformation subnetwork istrained to apply a transformation effect using adversarial training.

With reference to the first stage 805, the encoder 815 receives inputdata (not depicted in FIG. 8 ) and generates a deep-featurerepresentation of the input data, which is then input into the decoder820, which decodes the deep-feature representation back into areconstructed image. According to some example embodiments, feature loss830 is implemented to train the components of the first stage 805 in anend-to-end training procedure. Further, as illustrated in the exampleembodiment of FIG. 8 , the output from the decoder 820 can be encodedvia an encoder 825 that is configured to measure per-pixel imagesimilarity between the images (e.g., the image input into the encoder815 and the reconstructed image generated by decoder 820). The encoder825 further computes latent space features of the reconstructed imageand the latent space features of the input image, which are compared viathe feature loss 830. The overall similarity analyzed in the first stage805 is then the per-pixel similarity and the latent space featuresimilarity between the input and reconstructed image. In this way, byimplementing encoder 825, higher quality images are generated by thedeep-feature adversarial system 210. Further, according to some exampleembodiments, the deep-feature adversarial system 210 can be implementedwithout the additional encoder 825 and still yield quality output data.

In the second stage 810, the encoder 815 and decoder 820 are alreadytrained and held constant during the transformation subnetwork training,in which the network is trained to apply a particular transformationeffect. In the example embodiment illustrated, the input data (e.g., auser self-portrait image, “selfie”) is input into the now trainedencoder 815, which generates a deep-feature representation, which isthen transformed by the transformation subnetwork 835 into a transformeddeep-feature representation. The transformed deep-feature representationis then decoded back into image space to generate a reconstructed imageof the target domain which is then evaluated by the discrimination layer840, and loss training occurs by way of generative adversarial (GAN)loss 845.

After the two-stage training, the neural network model is then stored ortransmitted to client devices for implementation data, such as an image,an image sequence (e.g., video), audio, or other types of transformabledata. In some example embodiments, only the encoder 815, the decoder820, and the transformation subnetwork 835 are transmitted to clientdevices for implementation via neural network engine 620; that is, thediscrimination layer 840 may remain on the application server side(e.g., within server-side deep-feature adversarial system 150).

In some example embodiments, the encoder 815 and the decoder 820 areinitially transmitted to the client devices for storage in respectiveclient device memories. Then, at a later time, the transformationsubnetwork 835 trained for a certain transformation effect isdistributed to the client devices for incorporation between the encoder815 and decoder 820 in the respective client devices. This enables thegenerator to be trained once, and subsequent transformation trainingonly need be performed for the transformation subnetwork 835, which isthen transmitted to the client devices for application.

FIG. 9 shows an example deep-feature adversarial neural networkarchitecture 900 in first stage training, according to some exampleembodiments. As discussed above, in the first stage of training, thegenerator is trained. In the example illustrated, the generator 905(e.g., generator subnetwork) comprises an encoder 910 and a decoder 915.An image 920 is input into the encoder 910 that generates thedeep-feature space representation, which is then decoded back into areconstructed image 925 that is substantially identical to the inputimage 920.

In some example embodiments, the encoder 910 and the decoder 915 aretrained jointly at the same time for the same training task, such asimage inpainting or deblurring. However, the encoder 910 and decoder 915need not be trained jointly, and in some example embodiments, theencoder 910 is first trained for a first image classification, and thedecoder 915 is separately trained for a separate task (such as imagerecovery). Thus, the first stage training may include two separatesub-stages, including a encoder training sub-stage and a decodertraining sub-stage.

As illustrated, the generator 905 can be trained using dropout, which isa computational task that forces the decoder 915 to use information fromall layers, and not only the layer closest to the input image 920. Thedropout approach assumes corruption of some latent space features thatchange the features values to zero. In the example illustrated, thevalue of “0.25” is the percentage of corrupted values. When corruptionoccurs, the decoder 915 attempts to recover lost information from deeperlayers, thus achieving the result. However, it is possible to use othermethods, which achieve the same goal, such as adding Gaussian noise tofeatures instead of zeroing them, or jointly training theencoder-decoder pair for inpainting (reconstructing an original imagefrom the corrupted one, where some areas of the image were cut off ordamaged), deblurring (reconstructing the original image from itslow-resolution version), and so on.

FIG. 10 shows an example deep-feature adversarial neural networkarchitecture 1000 in second stage training, according to some exampleembodiments. In architecture 1000, the generator 905 has already beentrained as discussed above, and the transformation subnetwork 1005 istrained to transform a deep-feature representation from an initialdomain (e.g., a day time scene) to a target domain (e.g., a night timescene).

Architecturally, the transformation subnetwork 1005 comprises threeconvolutional layers A, B, and C. Each group of layers consists of 3convolutional layers, which use 3×3 kernels. The last layer of eachgroup produces a tensor of the same size and number of channels as theoutput tensor of the encoder, which serves as input for the first layerof this group, according to some example embodiments.

Further, as illustrated, an output of one group can serve as an inputfor another group (e.g., the output of “A” group layer is input into “B”group layer). As the input tensor sizes differ among groups (e.g., A, B,C), the output of a given layer is readjusted in size to be congruent asan input of another group. For example, the output of the last “A” grouplayer is a first size adjusted to match the “B” group layer, which isthen merged with encoder layer data and then the merged data is inputinto the “B” group layer; likewise, the output of the last “B” grouplayer is a size adjusted and then input into the first “C” group layer(e.g., after merging with encoder generated data). According to someexample embodiments, the inputs from a lower layer are merged with theinput of that layer. For example, the output of the group A merges withinput of the group B after the size adjustment transformation. In someexample embodiments, the size adjusts include non-parametric (such asnearest neighbor upsampling) adjustments, or parametric (size adjustmentis performed by another convolutional layer with trainable parameters)adjustments.

During training, the generator 905 receives an input image 1015 andgenerates a deep-feature representation in deep space that captures themiddle-level and high-level features of the image 1015 using the encoder910. The transformation subnetwork 1005 then transforms the deep-featurerepresentation from an initial domain to a target domain. The decoder915 then transforms the deep-feature space to pixel or image space togenerate a reconstructed image 1020 that exhibits the style of thetarget domain. The discriminator 1010 then analyzes the reconstructedimage 1020 and generates a prediction whether the reconstructed image1020 is a real or genuine data item of the target domain, or a fake dataitem that is not of the target domain, as denoted by prediction labels1025.

FIG. 11 shows an example user interface 1105 for generating an image andinitiating processing using a deep-feature adversarial system accordingto some example embodiments. In the example illustrated, a user 1100holding a client device 102 generates an image 1110, which is thendisplayed within a user interface 1105 generated by the interface engine605. The user 1100 then selects a button 1115 that initiatesdeep-feature adversarial system 210 to apply an aging effect to theimage 1110, which is a self-portrait of the user 1100.

With reference to FIG. 12 , in response to the selection of button 1115,the generator 905, which has been trained to apply an aging domaineffect, transforms the image 1110 into image 1200, which displays theself-portrait of the user 1100 in an aged or older domain, as indicatedby the wrinkles on the face. The user 1100 can then generate anephemeral message that comprises image 1200 and post the ephemeralmessage on a social network site by selecting button 1205.

FIG. 13 show an example user interface 1305 for modifying a domain of anaudio recording, according to some example embodiments. In the exampleillustrated, the user 1300 is holding a client device 102 that displaysa user interface 1305 generated by interface engine 605 of thedeep-feature adversarial system 210. The user 1300 selects button 1310to record music 1315 in a first style (e.g., solo vocal track) and storethe music 1315 as sound data in a memory of the client device 102. Theuser 1300 then selects button 1320 to trigger the deep-featureadversarial system 210 to apply a second musical style (e.g., salsamusic with salsa drums), which is then output via a transducer (e.g.,speaker) of the client device 102 as salsa music 1325. In some exampleembodiments, the recorded sound is stored as a stereogram, which isimage data displaying features of the audio track, as is appreciated bythose of ordinary skill in the art. The deep-feature adversarial system210 can transform the stereogram exhibiting the first style (e.g., solovocal track) to a target domain (e.g., salsa music) using theconvolutional transformation discussed above.

FIG. 14 is a block diagram illustrating an example software architecture1406, which may be used in conjunction with various hardwarearchitectures herein described. FIG. 14 is a non-limiting example of asoftware architecture, and it will be appreciated that many otherarchitectures may be implemented to facilitate the functionalitydescribed herein. The software architecture 1406 may execute on hardwaresuch as a machine 1500 of FIG. 15 that includes, among other things,processors, memory, and I/O components. A representative hardware layer1452 is illustrated and can represent, for example, the machine 1500 ofFIG. 15 . The representative hardware layer 1452 includes a processingunit 1454 having associated executable instructions 1404. The executableinstructions 1404 represent the executable instructions of the softwarearchitecture 1406, including implementation of the methods, components,and so forth described herein. The hardware layer 1452 also includes amemory/storage 1456, which also has the executable instructions 1404.The hardware layer 1452 may also comprise other hardware 1458.

In the example architecture of FIG. 14 , the software architecture 1406may be conceptualized as a stack of layers where each layer providesparticular functionality. For example, the software architecture 1406may include layers such as an operating system 1402, libraries 1420,frameworks/middleware 1418, applications 1416, and a presentation layer1415. Operationally, the applications 1416 and/or other componentswithin the layers may invoke API calls 1408 through the software stackand receive a response in the form of messages 1412. The layersillustrated are representative in nature and not all softwarearchitectures have all layers. For example, some mobile orspecial-purpose operating systems may not provide aframeworks/middleware 1418, while others may provide such a layer. Othersoftware architectures may include additional or different layers.

The operating system 1402 may manage hardware resources and providecommon services. The operating system 1402 may include, for example, akernel 1422, services 1424, and drivers 1426. The kernel 1422 may act asan abstraction layer between the hardware and the other software layers.For example, the kernel 1422 may be responsible for memory management,processor management (e.g., scheduling), component management,networking, security settings, and so on. The services 1424 may provideother common services for the other software layers. The drivers 1426are responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 1426 include display drivers, cameradrivers, Bluetooth® drivers, flash memory drivers, serial communicationdrivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi® drivers,audio drivers, power management drivers, and so forth depending on thehardware configuration.

The libraries 1420 provide a common infrastructure that is used by theapplications 1416 and/or other components and/or layers. The libraries1420 provide functionality that allows other software components toperform tasks in an easier fashion than by interfacing directly with theunderlying operating system 1402 functionality (e.g., kernel 1422,services 1424, and/or drivers 1426). The libraries 1420 may includesystem libraries 1444 (e.g., C standard library) that may providefunctions such as memory allocation functions, string manipulationfunctions, mathematical functions, and the like. In addition, thelibraries 1420 may include API libraries 1446 such as media libraries(e.g., libraries to support presentation and manipulation of variousmedia formats such as MPEG4, H.264, MP3, AAC, AMR, JPG, or PNG),graphics libraries (e.g., an OpenGL framework that may be used to render2D and 3D graphic content on a display), database libraries (e.g.,SQLite that may provide various relational database functions), weblibraries (e.g., WebKit that may provide web browsing functionality),and the like. The libraries 1420 may also include a wide variety ofother libraries 1448 to provide many other APIs to the applications 1416and other software components/modules.

The frameworks/middleware 1418 provides a higher-level commoninfrastructure that may be used by the applications 1416 and/or othersoftware components/modules. For example, the frameworks/middleware 1418may provide various graphic user interface (GUI) functions, high-levelresource management, high-level location services, and so forth. Theframeworks/middleware 1418 may provide a broad spectrum of other APIsthat may be utilized by the applications 1416 and/or other softwarecomponents/modules, some of which may be specific to a particularoperating system 1402 or platform.

The applications 1416 include built-in applications 1438 and/orthird-party applications 1440. Examples of representative built-inapplications 1438 may include, but are not limited to, a contactsapplication, a browser application, a book reader application, alocation application, a media application, a messaging application,and/or a game application. The third-party applications 1440 may includean application developed using the ANDROID™ or IOS™ software developmentkit (SDK) by an entity other than the vendor of the particular platform,and may be mobile software running on a mobile operating system such asIOS™, ANDROID™, WINDOWS® Phone, or other mobile operating systems. Thethird-party applications 1440 may invoke the API calls 1408 provided bythe mobile operating system (such as the operating system 1402) tofacilitate functionality described herein.

The applications 1416 may use built-in operating system functions (e.g.,kernel 1422, services 1424, and/or drivers 1426), libraries 1420, andframeworks/middleware 1418 to create user interfaces to interact withusers of the system. Alternatively, or additionally, in some systems,interactions with a user may occur through a presentation layer, such asthe presentation layer 1415. In these systems, the application/component“logic” can be separated from the aspects of the application/componentthat interact with a user.

FIG. 15 is a block diagram illustrating components of a machine 1500,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.Specifically, FIG. 15 shows a diagrammatic representation of the machine1500 in the example form of a computer system, within which instructions1516 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 1500 to perform any oneor more of the methodologies discussed herein may be executed. As such,the instructions 1516 may be used to implement modules or componentsdescribed herein. The instructions 1516 transform the general,non-programmed machine 1500 into a particular machine 1500 programmed tocarry out the described and illustrated functions in the mannerdescribed. In alternative embodiments, the machine 1500 operates as astandalone device or may be coupled (e.g., networked) to other machines.In a networked deployment, the machine 1500 may operate in the capacityof a server machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 1500 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a set-top box (STB), apersonal digital assistant (PDA), an entertainment media system, acellular telephone, a smartphone, a mobile device, a wearable device(e.g., a smart watch), a smart home device (e.g., a smart appliance),other smart devices, a web appliance, a network router, a networkswitch, a network bridge, or any machine capable of executing theinstructions 1516, sequentially or otherwise, that specify actions to betaken by the machine 1500. Further, while only a single machine 1500 isillustrated, the term “machine” shall also be taken to include acollection of machines that individually or jointly execute theinstructions 1516 to perform any one or more of the methodologiesdiscussed herein.

The machine 1500 may include processors 1510, memory/storage 1530, andI/O components 1550, which may be configured to communicate with eachother such as via a bus 1502. The memory/storage 1530 may include amemory 1532, such as a main memory or other memory storage, a staticmemory 1534, and a storage unit 1536, all accessible to the processors1510 such as via the bus 1502. The storage unit 1536 and memory 1532 andstatic memory 1534 store the instructions 1516 embodying any one or moreof the methodologies or functions described herein. The instructions1516 may also reside, completely or partially, within the memory 1532,within the static memory 1534, within the storage unit 1536, within atleast one of the processors 1510 (e.g., within the processor cachememory accessible to processors 1512 or 1514), or any suitablecombination thereof, during execution thereof by the machine 1500.Accordingly, the memory 1532, the static memory 1534, the storage unit1536, and the memory of the processors 1510 are examples ofmachine-readable media.

The I/O components 1550 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 1550 that are included in a particular machine 1500 willdepend on the type of machine. For example, portable machines such asmobile phones will likely include a touch input device or other suchinput mechanisms, while a headless server machine will likely notinclude such a touch input device. It will be appreciated that the I/Ocomponents 1550 may include many other components that are not shown inFIG. 15 . The I/O components 1550 are grouped according to functionalitymerely for simplifying the following discussion and the grouping is inno way limiting. In various example embodiments, the I/O components 1550may include output components 1552 and input components 1554. The outputcomponents 1552 may include visual components (e.g., a display such as aplasma display panel (PDP), a light-emitting diode (LED) display, aliquid-crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), haptic components (e.g., avibratory motor, resistance mechanisms), other signal generators, and soforth. The input components 1554 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or other pointinginstruments), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

In further example embodiments, the I/O components 1550 may includebiometric components 1556, motion components 1558, environmentcomponents 1560, or position components 1562 among a wide array of othercomponents. For example, the biometric components 1556 may includecomponents to detect expressions (e.g., hand expressions, facialexpressions, vocal expressions, body gestures, or eye tracking), measurebiosignals (e.g., blood pressure, heart rate, body temperature,perspiration, or brain waves), identify a person (e.g., voiceidentification, retinal identification, facial identification,fingerprint identification, or electroencephalogram-basedidentification), and the like. The motion components 1558 may includeacceleration sensor components (e.g., accelerometer), gravitation sensorcomponents, rotation sensor components (e.g., gyroscope), and so forth.The environment components 1560 may include, for example, illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometers that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gassensors to detect concentrations of hazardous gases for safety or tomeasure pollutants in the atmosphere), or other components that mayprovide indications, measurements, or signals corresponding to asurrounding physical environment. The position components 1562 mayinclude location sensor components (e.g., a GPS receiver component),altitude sensor components (e.g., altimeters or barometers that detectair pressure from which altitude may be derived), orientation sensorcomponents (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1550 may include communication components 1564operable to couple the machine 1500 to a network 1580 or devices 1570via a coupling 1582 and a coupling 1572, respectively. For example, thecommunication components 1564 may include a network interface componentor other suitable device to interface with the network 1580. In furtherexamples, the communication components 1564 may include wiredcommunication components, wireless communication components, cellularcommunication components, near field communication (NFC) components,Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components,and other communication components to provide communication via othermodalities. The devices 1570 may be another machine or any of a widevariety of peripheral devices (e.g., a peripheral device coupled via aUSB).

Moreover, the communication components 1564 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 1564 may include radio frequency identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional barcodes such as Universal Product Code (UPC) barcode,multi-dimensional barcodes such as Quick Response (QR) code, Aztec code,Data Matrix, Dataglyph, MaxiCode, PDF418, Ultra Code, UCC RSS-2Dbarcode, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components1564, such as location via Internet Protocol (IP) geolocation, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

“CARRIER SIGNAL” in this context refers to any intangible medium that iscapable of storing, encoding, or carrying instructions 1516 forexecution by the machine 1500, and includes digital or analogcommunications signals or other intangible media to facilitatecommunication of such instructions 1516. Instructions 1516 may betransmitted or received over the network 1580 using a transmissionmedium via a network interface device and using any one of a number ofwell-known transfer protocols.

“CLIENT DEVICE” in this context refers to any machine 1500 thatinterfaces to a communications network 1580 to obtain resources from oneor more server systems or other client devices 102. A client device 102may be, but is not limited to, a mobile phone, desktop computer, laptop,PDA, smartphone, tablet, ultrabook, netbook, multi-processor system,microprocessor-based or programmable consumer electronics system, gameconsole, set-top box, or any other communication device that a user mayuse to access a network 1580.

“COMMUNICATIONS NETWORK” in this context refers to one or more portionsof a network 1580 that may be an ad hoc network, an intranet, anextranet, a virtual private network (VPN), a local area network (LAN), awireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), ametropolitan area network (MAN), the Internet, a portion of theInternet, a portion of the Public Switched Telephone Network (PSTN), aplain old telephone service (POTS) network, a cellular telephonenetwork, a wireless network, a Wi-Fi® network, another type of network,or a combination of two or more such networks. For example, a network ora portion of a network 1580 may include a wireless or cellular networkand the coupling may be a Code Division Multiple Access (CDMA)connection, a Global System for Mobile communications (GSM) connection,or another type of cellular or wireless coupling. In this example, thecoupling may implement any of a variety of types of data transfertechnology, such as Single Carrier Radio Transmission Technology(1×RTT), Evolution-Data Optimized (EVDO) technology, General PacketRadio Service (GPRS) technology, Enhanced Data rates for GSM Evolution(EDGE) technology, third Generation Partnership Project (3GPP) including3G, fourth generation wireless (4G) networks, Universal MobileTelecommunications System (UMTS), High-Speed Packet Access (HSPA),Worldwide Interoperability for Microwave Access (WiMAX), Long-TermEvolution (LTE) standard, others defined by various standard-settingorganizations, other long-range protocols, or other data transfertechnology.

“EPHEMERAL MESSAGE” in this context refers to a message 400 that isaccessible for a time-limited duration. An ephemeral message 502 may bea text, an image, a video, and the like. The access time for theephemeral message 502 may be set by the message sender. Alternatively,the access time may be a default setting or a setting specified by therecipient. Regardless of the setting technique, the message 400 istransitory.

“MACHINE-READABLE MEDIUM” in this context refers to a component, adevice, or other tangible media able to store instructions 1516 and datatemporarily or permanently and may include, but is not limited to,random-access memory (RAM), read-only memory (ROM), buffer memory, flashmemory, optical media, magnetic media, cache memory, other types ofstorage (e.g., erasable programmable read-only memory (EPROM)), and/orany suitable combination thereof. The term “machine-readable medium”should be taken to include a single medium or multiple media (e.g., acentralized or distributed database, or associated caches and servers)able to store instructions 1516. The term “machine-readable medium”shall also be taken to include any medium, or combination of multiplemedia, that is capable of storing instructions 1516 (e.g., code) forexecution by a machine 1500, such that the instructions 1516, whenexecuted by one or more processors 1510 of the machine 1500, cause themachine 1500 to perform any one or more of the methodologies describedherein. Accordingly, a “machine-readable medium” refers to a singlestorage apparatus or device, as well as “cloud-based” storage systems orstorage networks that include multiple storage apparatus or devices. Theterm “machine-readable medium” excludes signals per se.

“COMPONENT” in this context refers to a device, a physical entity, orlogic having boundaries defined by function or subroutine calls, branchpoints, APIs, or other technologies that provide for the partitioning ormodularization of particular processing or control functions. Componentsmay be combined via their interfaces with other components to carry outa machine process. A component may be a packaged functional hardwareunit designed for use with other components and a part of a program thatusually performs a particular function of related functions. Componentsmay constitute either software components (e.g., code embodied on amachine-readable medium) or hardware components. A “hardware component”is a tangible unit capable of performing certain operations and may beconfigured or arranged in a certain physical manner. In various exampleembodiments, one or more computer systems (e.g., a standalone computersystem, a client computer system, or a server computer system) or one ormore hardware components of a computer system (e.g., a processor 1512 ora group of processors 1510) may be configured by software (e.g., anapplication or application portion) as a hardware component thatoperates to perform certain operations as described herein. A hardwarecomponent may also be implemented mechanically, electronically, or anysuitable combination thereof. For example, a hardware component mayinclude dedicated circuitry or logic that is permanently configured toperform certain operations. A hardware component may be aspecial-purpose processor, such as a field-programmable gate array(FPGA) or an application-specific integrated circuit (ASIC). A hardwarecomponent may also include programmable logic or circuitry that istemporarily configured by software to perform certain operations. Forexample, a hardware component may include software executed by ageneral-purpose processor or other programmable processor. Onceconfigured by such software, hardware components become specificmachines (or specific components of a machine 1500) uniquely tailored toperform the configured functions and is no longer general-purposeprocessors 1510. It will be appreciated that the decision to implement ahardware component mechanically, in dedicated and permanently configuredcircuitry, or in temporarily configured circuitry (e.g., configured bysoftware) may be driven by cost and time considerations. Accordingly,the phrase “hardware component” (or “hardware-implemented component”)should be understood to encompass a tangible entity, be that an entitythat is physically constructed, permanently configured (e.g.,hardwired), or temporarily configured (e.g., programmed) to operate in acertain manner or to perform certain operations described herein.

Considering embodiments in which hardware components are temporarilyconfigured (e.g., programmed), each of the hardware components need notbe configured or instantiated at any one instance in time. For example,where a hardware component comprises a general-purpose processor 1512configured by software to become a special-purpose processor, thegeneral-purpose processor 1512 may be configured as respectivelydifferent special-purpose processors (e.g., comprising differenthardware components) at different times. Software accordingly configuresa particular processor 1512 or processors 1510, for example, toconstitute a particular hardware component at one instance of time andto constitute a different hardware component at a different instance oftime.

Hardware components can provide information to, and receive informationfrom, other hardware components. Accordingly, the described hardwarecomponents may be regarded as being communicatively coupled. Wheremultiple hardware components exist contemporaneously, communications maybe achieved through signal transmission (e.g., over appropriate circuitsand buses) between or among two or more of the hardware components. Inembodiments in which multiple hardware components are configured orinstantiated at different times, communications between or among suchhardware components may be achieved, for example, through the storageand retrieval of information in memory structures to which the multiplehardware components have access. For example, one hardware component mayperform an operation and store the output of that operation in a memorydevice to which it is communicatively coupled. A further hardwarecomponent may then, at a later time, access the memory device toretrieve and process the stored output. Hardware components may alsoinitiate communications with input or output devices, and can operate ona resource (e.g., a collection of information).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors 1510 that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors 1510 may constitute processor-implementedcomponents that operate to perform one or more operations or functionsdescribed herein. As used herein, “processor-implemented component”refers to a hardware component implemented using one or more processors1510. Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor 1512 or processors1510 being an example of hardware. For example, at least some of theoperations of a method may be performed by one or more processors 1510or processor-implemented components. Moreover, the one or moreprocessors 1510 may also operate to support performance of the relevantoperations in a “cloud computing” environment or as a “software as aservice” (SaaS). For example, at least some of the operations may beperformed by a group of computers (as examples of machines 1500including processors 1510), with these operations being accessible via anetwork 1580 (e.g., the Internet) and via one or more appropriateinterfaces (e.g., an API). The performance of certain of the operationsmay be distributed among the processors 1510, not only residing within asingle machine 1500, but deployed across a number of machines 1500. Insome example embodiments, the processors 1510 or processor-implementedcomponents may be located in a single geographic location (e.g., withina home environment, an office environment, or a server farm). In otherexample embodiments, the processors 1510 or processor-implementedcomponents may be distributed across a number of geographic locations.

“PROCESSOR” in this context refers to any circuit or virtual circuit (aphysical circuit emulated by logic executing on an actual processor1512) that manipulates data values according to control signals (e.g.,“commands,” “op codes,” “machine code,” etc.) and which producescorresponding output signals that are applied to operate a machine 1500.A processor may, for example, be a central processing unit (CPU), areduced instruction set computing (RISC) processor, a complexinstruction set computing (CISC) processor, a graphics processing unit(GPU), a digital signal processor (DSP), an ASIC, a radio-frequencyintegrated circuit (RFIC), or any combination thereof. A processor 1510may further be a multi-core processor 1510 having two or moreindependent processors 1512, 1514 (sometimes referred to as “cores”)that may execute instructions 1516 contemporaneously.

“TIMESTAMP” in this context refers to a sequence of characters orencoded information identifying when a certain event occurred, forexample giving date and time of day, sometimes accurate to a smallfraction of a second.

What is claimed is:
 1. A method comprising: identifying an image on adevice; generating, by a processor of the device, a modified image byapplying an adversarial neural network to the image, the adversarialneural network comprising an adversarial transformation network within agenerator network, and the generator network further comprising anencoder network and a decoder network, wherein the adversarial neuralnetwork is trained in stages comprising a first stage, in which at leastone of the encoder network or the decoder network are trained, and asecond stage, in which the adversarial transformation network istrained, the adversarial transformation network not being trained in thefirst stage; and publishing, by the device, the modified image to anetwork site as an ephemeral message.
 2. The method of claim 1, furthercomprising: identifying an instruction to apply an image effect to theimage; and storing the modified image.
 3. The method of claim 2, whereinthe adversarial transformation network is between the encoder networkand the decoder network.
 4. The method of claim 3, wherein theadversarial transformation network is adversarially trained using adiscrimination network that receives output data from the decodernetwork.
 5. The method of claim 3, wherein the adversarialtransformation network is trained using a discrimination network.
 6. Themethod of claim 5, wherein in the first stage the encoder network andthe decoder network are jointly trained using feature loss.
 7. Themethod of claim 5, wherein the encoder network and the decoder networkare not trained in the second stage.
 8. The method of claim 5, whereinthe adversarial transformation network is trained using GenerativeAdversarial Loss (GAN) loss.
 9. The method of claim 2, furthercomprising: receiving, from a server, the adversarial transformationnetwork pre-trained to apply the image effect.
 10. The method of claim2, further comprising: receiving, from an input interface of the device,the instruction to apply the image effect on the image.
 11. The methodof claim 10, further comprising: in response to receiving theinstruction, selecting, from a plurality of neural networks stored onthe device, the adversarial neural network based on the adversarialneural network being trained for the image effect specified by theinstruction, wherein each of the plurality of neural networks is trainedfor different image effects using adversarial transformation networkswithin respective generator networks.
 12. The method of claim 1, whereinthe adversarial transformation network is trained to transfer an imagebetween different domains.
 13. The method of claim 12, wherein thedomains includes one or more of the following: an old person domain, ayoung person domain, a masculine person domain, a feminine persondomain, a painted domain, a photorealistic domain.
 14. The method ofclaim 1, wherein the adversarial neural network is a convolutionalneural network.
 15. The method of claim 1, further comprising:generating the image using an image sensor of the device.
 16. The methodof claim 1, wherein, in the second stage, the adversarial transformationnetwork is trained to apply a transformation effect to input data.
 17. Adevice comprising: one or more processors; an image sensor; and a memorystoring instructions that, when executed by the one or more processors,cause the device to perform operations comprising: identifying an imageon the device; generating, by a processor of the device, a modifiedimage by applying an adversarial neural network to the image, theadversarial neural network comprising an adversarial transformationnetwork within a generator network, and the generator network furthercomprising an encoder network and a decoder network, wherein theadversarial neural network is trained in stages comprising a firststage, in which at least one of the encoder network or the decodernetwork are trained, and a second stage, in which the adversarialtransformation network is trained, the adversarial transformationnetwork not being trained in the first stage; and publishing, by thedevice, the modified image to a network site as an ephemeral message.18. The device of claim 17, wherein the operations further comprise:identifying an instruction to apply an image effect to the image; andstoring the modified image.
 19. The device of claim 18, wherein theadversarial transformation network is between the encoder network andthe decoder network.
 20. A non-transitory machine-readable storagedevice embodying instructions that, when executed by a device, cause thedevice to perform operations comprising: identifying an image on thedevice; generating, by a processor of the device, a modified image byapplying an adversarial neural network to the image, the adversarialneural network comprising an adversarial transformation network within agenerator network, and the generator network further comprising anencoder network and a decoder network, wherein the adversarial neuralnetwork is trained in stages comprising a first stage, in which at leastone of the encoder network or the decoder network are trained, and asecond stage, in which the adversarial transformation network istrained, the adversarial transformation network not being trained in thefirst stage; and publishing, by the device, the modified image to anetwork site as an ephemeral message.